use _to_copy or _to_dim_order_copy in I64toI32 pass (pytorch#3968)

Summary:
Pull Request resolved: pytorch#3968

When converting an ExportedProgram to EdgeProgramManager via to_edge, _to_dim_order_copy or _to_copy will be used depending on the passed EdgeCompileConfig having _skip_dim_order set to true or false. For example:
```
compile_config=EdgeCompileConfig(
    _check_ir_validity=False,
    _skip_dim_order=True,
),
```
The newer _to_dim_order_copy op was added in D53747744/pytorch#1970.

Adding a param to the pass init to determine if we use the newer op or not.

Reviewed By: copyrightly

Differential Revision: D58395616

fbshipit-source-id: 8f5289f8ed62a5c3814ec32b91fb055a10944fdb

Author: nathanaelsee

backends/transforms/i64_to_i32.py

@@ -14,6 +14,14 @@
 
 class I64toI32(ExportPass):
 
+    def __init__(self, _skip_dim_order=False):
+        super(I64toI32, self).__init__()
+        self.copy_op = (
+            exir_ops.edge.aten._to_copy.default
+            if _skip_dim_order
+            else exir_ops.edge.dim_order_ops._to_dim_order_copy.default
+        )
+
     def _is_i64_tensor(self, node_val):
         return isinstance(node_val, FakeTensor) and node_val.dtype == torch.int64
 
@@ -34,7 +42,7 @@ def _apply_to_i32(self, graph: torch.fx.Graph):
                         args = (node,)
                         node_i32 = graph.create_node(
                             "call_function",
-                            exir_ops.edge.dim_order_ops._to_dim_order_copy.default,
+                            self.copy_op,
                             args,
                             {"dtype": torch.int32},
                         )
@@ -62,7 +70,7 @@ def _apply_to_i32(self, graph: torch.fx.Graph):
                             args = (node.args[0][i],)
                             node_i64 = graph.create_node(
                                 "call_function",
-                                exir_ops.edge.dim_order_ops._to_dim_order_copy.default,
+                                self.copy_op,
                                 args,
                                 {"dtype": torch.int64},
                             )